Optimizing Key Distribution in Peer to Peer Network Using B-Trees

Peer to peer network architecture introduces many desired features including self-scalability that led to achieving higher efficiency rate than the traditional server-client architecture. This was contributed to the highly distributed architecture of peer to peer network. Meanwhile, the lack of a centralized control unit in peer to peer network introduces some challenge. One of these challenges is key distribution and management in such an architecture. This research will explore the possibility of developing a novel scheme for distributing and managing keys in peer to peer network architecture efficiently

Administration Security Issues in Cloud Computing

This paper discover the most administration security issues in Cloud Computing in term of trustworthy and gives the reader a big visualization of the concept of the Service Level Agreement in Cloud Computing and it’s some security issues. Finding a model that mostly guarantee that the data be saved secure within setting for factors which are data location, duration of keeping the data in cloud environment, trust between customer and provider, and procedure of formulating the SLA

Improving Hadoop Performance by Using Metadata of Related Jobs in Text Datasets Via Enhancing MapReduce Workflow

Cloud Computing provides different services to the users with regard to processing data. One of the main concepts in Cloud Computing is BigData and BigData analysis. BigData is a complex, un-structured or very large size of data. Hadoop is a tool or an environment that is used to process BigData in parallel processing mode. The idea behind Hadoop is, rather than send data to the servers to process. Hadoop divides a job into small tasks and sends them to servers. These servers contain data, process the tasks and send the results back to the master node in Hadoop. Hadoop contains some limitations that could be developed to have a higher performance in executing jobs. These limitations are mostly because of data locality in the cluster, jobs and tasks scheduling, CPU execution time, or resource allocations in Hadoop. Data locality and efficient resource allocation remains a challenge in cloud computing MapReduce platform. We propose an enhanced Hadoop architecture that reduces the computation cost associated with BigData analysis. At the same time, the proposed architecture addresses the issue of resource allocation in native Hadoop. The proposed architecture provides an efficient distributed clustering approach for dedicated cloud computing environments. Enhanced Hadoop architecture leverages on NameNode’s ability to assign jobs to the TaskTrakers (DataNodes) within the cluster. By adding controlling features to the NameNode, it can intelligently direct and assign tasks to the DataNodes that contain the required data. Our focus is on extracting features and building a metadata table that carries information about the existence and the location of the data blocks in the cluster. This enables NameNode to direct the jobs to specific DataNodes without going through the whole data sets in the cluster. It should be noted that newly build lookup table is an addition to the metadata table that already exists in the native Hadoop. Our development is about processing real text in text data sets that might be readable such as books, or not readable such as DNA data sets. To test the performance of proposed architecture, we perform DNA sequence matching and alignment of various short genome sequences. Comparing with native Hadoop, proposed Hadoop reduced CPU time, number of read operations, input data size, and another different factors

Enhancing Hadoop MapReduce Performance for Scientific Data using NoSQL Database

Scientific data sets usually have similar jobs that are frequently applied to the data by different users. In addition, many of these data sets are unstructured, complex, and required fast and simple processing. In order to increase the performance of the existing Hadoop and MapReduce algorithm, it is necessary to develop an algorithm based on the type of data sets and requirements of the jobs. In this poster, we represent a Hadoop MapReduce environment that uses genomic and biological data as an example of unstructured and complex data

Understanding the work of hospital managers in the public sector in Saudi Arabia

This paper reports some of the preliminary findings of research that proposes to explore the nature of the hospital managers' work, their opinions about the most essential functions, roles, skills and training courses needed to manage the Ministry of Health (MOH) hospitals in Saudi Arabia (SA). It intends to describe four types of hospital managers according to their educational background and managerial experience. Self-administered postal questionnaires were distributed to 218 managers working in MOH hospitals across the country. The use of postal survey allowed coverage of a sample across a wide geographical region. 72.9% of questionnaires were completed, which were valid for descriptive and univariate analysis

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps, for the miss-valued attributes, we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes. However, in order to ensure the model optimality, we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers. The preprocessing step led to 161 out of 279 attributes (features). Thereafter, a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms. In short, our study comprises three main blocks: (1) sensing data and preprocessing; (2) feature queuing, selection, and extraction; and (3) the predictive model. Our proposed method improves classification performance in terms of accuracy, F1 measure, recall, and precision when compared to state-of-the-art techniques. It achieves 98.5% accuracy for binary class mode and 98.9% accuracy for categorized class mode

Systematic Review in Patients with Benign Prostatic Hyperplasia for The Role of Prostatic Arterial Embolization

This study aimed at reviewing the role of Prostatic Arterial Embolization (PAE) as a new treatment producer for patients with Benign Prostatic Hyperplasia (BPH). The study reviewed the recent researches of  Prostatic Arterial Embolization (PAE) as a new treatment technique and concluded that the initial reported results of PAE seem promising, mainly during the first 12 months after treatment. However, no comparison was made to medical therapy or surgical therapies. Overlapping patient data and reporting bias could not be excluded. None of the included studies performed a power analysis. Also, a relatively small number of patients are treated with a short follow-up period. Therefore, more studies are needed with more patients and longer periods of follow-up, compared with standard medical and surgical therapies, to assess whether PAE is an effective and safe alternative treatment for BPH

Pediatric Dermatology In Family Medicine: Common Conditions And Management Strategies

Among the most prevalent disorders are those related to the skin.  However, in medical education and training, this class of illnesses is frequently disregarded. The first line of defence for the treatment of common dermatological diseases is a family physician. The purpose of our study was to evaluate the particular identification, management, encountering, and referral practices related to dermatological illnesses in family care. We also looked into the challenges and opportunities that family doctors experience in family medicine and saw a few of the paediatric dermatological diseases that family doctors may encounter.  Finding areas of weakness in the clinical therapy of certain dermatological disorders, however, will be aided by assessing how family doctors treat particular ailments. Thus, this needs assessment might serve as a foundation for future research on the efficacy of family medicine in treating common paediatric dermatological problems as well as aid in the development of evidence-based training for family physicians in the area

Classification of MRI brain tumors based on registration preprocessing and deep belief networks

In recent years, augmented reality has emerged as an emerging technology with huge potential in image-guided surgery, and in particular, its application in brain tumor surgery seems promising. Augmented reality can be divided into two parts: hardware and software. Further, artificial intelligence, and deep learning in particular, have attracted great interest from researchers in the medical field, especially for the diagnosis of brain tumors. In this paper, we focus on the software part of an augmented reality scenario. The main objective of this study was to develop a classification technique based on a deep belief network (DBN) and a softmax classifier to (1) distinguish a benign brain tumor from a malignant one by exploiting the spatial heterogeneity of cancer tumors and homologous anatomical structures, and (2) extract the brain tumor features. In this work, we developed three steps to explain our classification method. In the first step, a global affine transformation is preprocessed for registration to obtain the same or similar results for different locations (voxels, ROI). In the next step, an unsupervised DBN with unlabeled features is used for the learning process. The discriminative subsets of features obtained in the first two steps serve as input to the classifier and are used in the third step for evaluation by a hybrid system combining the DBN and a softmax classifier. For the evaluation, we used data from Harvard Medical School to train the DBN with softmax regression. The model performed well in the classification phase, achieving an improved accuracy of 97.2%

Prevalence of Endocrine Disorders Among Down Syndrome Individuals in Ksa: A Cross-Sectional Study

Objective: To determine the prevalence of endocrine disorders among individuals with Down Syndrome in KSA. Methods: This research employs a cross-sectional study design to investigate the prevalence of endocrine disorders among individuals with Down Syndrome in the Kingdom of Saudi Arabia (KSA). A cross-sectional approach allows us to collect data at a single point in time from a diverse group of participants, providing a snapshot of the prevalence and characteristics of endocrine disorders within the study population. Results: The study included 686 participants. The participants asked if they had a child with Down syndrome. Most of them answered no (n= 576, 84%) followed by yes (n= 110, 16%). The most frequent child age who has Down syndrome among study participants was 7-10 years (n= 45, 40.9%) followed by 3-6 years (n= 30, 27.3%). The most frequent child gender who has Down syndrome among study participants was female (n= 57, 51.8%) followed by male (n= 53, 48.2%). Father's educational level among study participants with most of them having a university (n= 82, 74.5%). Mother's educational level among study participants with most of them having a university (n= 77, 70%). Participants were asked if there was a first-degree relationship between the parents. There 55 had a first-degree relationship with (50%), and 55 didn’t have a first-degree relationship between parents with (50%). Participants were asked the female about two diseases polycystic ovary disease there were 12 had it (10.9%), 62 didn’t have it (56.4%), and the second disease was Turner syndrome 22 had it (20%) and 53 participants didn’t have it (47.3%). Conclusion: Study results showed that most of the study participants don’t have Down Syndrome according to the parent's answers. Half of the participants have a first-degree relationship between their parents. The most educational level for parents was the university